AI Health Underwriting Questions
Prepare the answers before approaching the market, especially where the tool processes consultations or sits near clinical work. These questions come from real submissions, not a governance framework.
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The Tank take
Insurers assessing AI health tools focus on intended use, users, clinical reliance, model validation, human review, data, vendors, security and correlated failure. A technically detailed but clinically vague submission still attracts every one of these questions.
The strongest submissions connect each technical control to the harm it prevents. Generic AI governance statements do not do that; specific answers about your workflow do.
Purpose, validation and clinical reliance
These establish what the tool is and how wrong it can be.
- 01
Who uses the tool, in what setting and for what intended purpose?
- 02
What pilots, beta tests or clinical evaluations support it?
- 03
How are accuracy, bias, translation and hallucination risks measured?
- 04
Who reviews outputs, what can they change, and how are unsafe results escalated?
- 05
Can an output influence a diagnosis, treatment or safety decision?
- 06
How are model updates evaluated before release?
Data, dependencies and failure
These establish what happens when something breaks or leaks.
- 01
Is the model owned, fine-tuned or supplied by a third party?
- 02
Can personal or sensitive health information be used for model training?
- 03
What consent, retention, location and deletion controls govern the data?
- 04
How are tenants separated, and can one customer's incident affect others?
- 05
What happens when a model, API or hosting region fails?
- 06
What rollback, incident response and customer communication plans exist?
These reflect issues specialist insurers commonly test for AI-assisted health tools. Adapt them to the product rather than answering with generic governance language.
Turning answers into a placement
Map the workflow
Document every input, model, vendor, human review point, output and downstream decision. This map answers half the questionnaire on its own.
Package the validation
Test design, populations, acceptance criteria, results and change control, presented as evidence rather than assertion.
Rehearse the failure story
Degraded modes, escalation, rollback, downtime and communication - explained to underwriters before they ask.
Check the current Australian guidance
Regulatory obligations sit outside your insurance policy. These official sources are the starting point.
External government and industry sources. Tank Insurance is not responsible for their content; confirm current requirements with the relevant body.
Related life sciences guides
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Questions about AI Underwriting Questions
No. The evidence expected depends on intended purpose and clinical consequence. Administrative tools and diagnostic software carry different validation expectations.
It shows whether one customer's security or data issue can affect others, and how correlated loss is contained. Multi-tenant platforms that cannot answer this get priced for the aggregation.
Contracts may allocate some liability, but the insured usually remains responsible to its own customers and users for the service it delivers.
Material changes to intended use, model, clinical workflow, countries or exposure may need notification under the policy. Build it into the release process.
General information only. This page does not take account of your objectives, financial situation or needs and is not legal advice. Cover depends on the insurer, policy wording, limits, excesses, exclusions and information disclosed. Read the relevant policy documents and obtain professional advice before deciding.
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